Unconstrained tracking MPC for continuous-time nonlinear systems

In this paper, we extend unconstrained model predictive control (MPC) from setpoint stabilization to dynamic reference tracking for continuous-time nonlinear systems. In particular, we focus on the case when the reference cannot be perfectly tracked by the system due to dynamics and/or constraints. Under the incremental stabilizability assumption and an additional dissipativity assumption, the practical stability of tracking the unknown optimal reachable reference trajectory is proved even though the controller does not know such a reference explicitly.Agency for Science, Technology and Research (A*STAR)National Research Foundation (NRF)The work of Long and Xie is funded by the Republic of Singapore’s National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore. The work of Long is also funded by A*ccelerate Gap-funded Project No. ACCL200013